System and method to anonymize data transmitted to a destination computing device

ABSTRACT

A method and system for anonymizing data to be transmitted to a destination computing device is disclosed. Data to be transmitted is received from a user computer. The data includes one or more characters. The data is replaced with a token representative of the data. The token is transmitted to the destination computing device over a network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of application Ser. No. 13/042,459 filed on Mar. 8, 2011, which is incorporated herein by reference, in its entirety. This application is also a continuation-in-part of application Ser. No. 13/323,821 filed on Dec. 13, 2011, which is incorporated herein by reference, in its entirety.

TECHNICAL FIELD

The present invention relates generally to transmitting data to and retrieving data from a destination computing device and particularly, to anonymize data transmitted to the destination computing device and de-anonymize data retrieved from the destination computing device.

DESCRIPTION OF RELATED ART

The Internet has enabled computer users all over the world to interact, communicate, store and access information electronically. One particularly popular mode for running applications over the internet is to host application on a server located in a remote location, along with data associated with the applications. The users of the application access the application and associated data over the internet. Sometimes, this mode of running applications in a remote location is referred to as cloud computing. It is not uncommon for the application to be hosted on a server owned or controlled by a third party. And, various clients or users of the application run the application hosted on the server at the third party location and store data corresponding to the application on a data storage device, for example, a database running on a computing device at the third party location.

There are multiple benefits of using a third party hosted application to a client. For example, the application may be managed by subject matter experts who are familiar with the application, for example a customer relationship management (CRM) application. Sometimes, ancillary applications and resources needed to provide best in class application may be made available to all the clients of the application. The third party may be able to allocate resources based on varying or seasonal demands of each of its customers.

As more and more enterprises or customers use applications hosted at third party locations, the data associated with the enterprise may need to be protected from unauthorized access. Some of the data security requirements may be imposed by regulatory bodies. Some of the data security requirement may be client specific.

As more and more applications are hosted at the third party locations and corresponding data is stored at the third party locations, there is a need to find efficient ways to provide data security to the data stored at the third party locations. With these needs in mind, the current disclosure arises. This brief summary has been provided so that the nature of the disclosure may be understood quickly. A more complete understanding of the disclosure can be obtained by reference to the following detailed description of the various embodiments thereof in connection with the attached drawings.

SUMMARY OF THE INVENTION

In one embodiment, a method for anonymizing data to be transmitted to a destination computing device is disclosed. Data to be transmitted is received from a user computer. The data includes one or more characters. The data is replaced with a token representative of the data. The token is transmitted to the destination computing device over a network.

In yet another embodiment, an anonymization system to anonimize data transmitted to a destination computing device is disclosed. The system includes a logic to receive data to be transmitted to the destination computing device from a user computer, the data including one or more characters. An anonymization module replaces the data with a token representative of the data and transmit the token to the destination computing device over a network.

This brief summary has been provided so that the nature of the disclosure may be understood quickly. A more complete understanding of the disclosure can be obtained by reference to the following detailed description of the preferred embodiments thereof in connection with the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features of several embodiments are now described with reference to the drawings. In the drawings, the same components have the same reference numerals. The illustrated embodiments are intended to illustrate but not limit the invention. The drawings include the following Figures:

FIG. 1A shows a block diagram of a system with anonymization system of the present disclosure used to process requests from a user system to an application server, according to one aspect of the present disclosure;

FIG. 1B shows a block diagram of the user system of FIG. 1 configured to communicate with a server over the network, according to one aspect of the present disclosure;

FIG. 2 shows a block diagram of the internal functional architecture of a user computer, according to one aspect of the present disclosure;

FIG. 2A shows a block diagram of another embodiment of a system used to service requests from a user computer, according to another aspect of the present disclosure;

FIG. 3 shows an exemplary topology of computer network coupling user computers to servers over the internet, according to one aspect of the present disclosure;

FIGS. 4 and 4A show an exemplary block diagram of an anonymization system, according to one aspect of the present disclosure;

FIG. 5 shows an exemplary anonymization strategy data table, according to one aspect of the present invention;

FIG. 6A shows an exemplary block diagram of a searchable encryption system, according to one aspect of the present disclosure;

FIG. 6B shows an exemplary table 620 showing various iterations of the encoding operation of the searchable encryption system of FIG. 6A, according to one aspect of the present disclosure.

FIG. 6C shows an exemplary table 650 showing various iterations of the decoding operation of the searchable encryption system of FIG. 6A, according to one aspect of the present disclosure.

FIG. 7A shows an exemplary tree to generate tokens for various data in a data field, according to one aspect of the present disclosure;

FIG. 7B shows an exemplary table to describe exemplary encoding of data represented by a node, according to one aspect of the present disclosure;

FIG. 7C shows an exemplary table with various attributes of a node, according to one aspect of the present disclosure;

FIG. 7D shows exemplary table of 7C with various attributes of another node, according to one aspect of the present disclosure;

FIG. 8A-8D shows exemplary anonymization of data transmitted to a destination computing device;

FIGS. 9A-9C shows exemplary de-anomymization of data received from the destination computing device;

FIG. 10 shows method of anonymizing data to be transmitted to a destination computing, according to one aspect of the present disclosure;

FIG. 11A shows method of processing a request for stored anonymized data by the destination computing device; and

FIG. 11B shows method of processing selectively retrieved anonymized data from the destination computing device.

DETAILED DESCRIPTION

To facilitate an understanding of the adaptive aspects of the present invention, the general architecture and operation of a networked system is described. The specific architecture and operation of the adaptive aspects of the present disclosure are then described with reference to the general architecture.

FIG. 1A shows a top-level block diagram of a system 100 with anonymization system of this disclosure that is used to send data from a user system, according to one aspect of the present disclosure. System 100 includes a user system 102 that is coupled via a gatway 104 and a network connection 106 to a server 110 through another gateway 108. In one aspect, server 110 is a web-server. Gateway 104 in one embodiment includes an anonymization system 112 of this disclosure. Database 114 is used to store information that is accessed by the user system 102. In one aspect, database 114 is a structured query language (SQL) based database.

FIG. 1B is a block diagram of a user system 102 according to one embodiment of the present disclosure, which is configured to communicate with the server 110 over the network. FIG. 1B includes a user computer 116 (sometimes referred to as computer 116) and a monitor 118. Monitor 118 may be a CRT type, a LCD type, a plasma type, or any other type of color or monochrome display. Also provided with computer 116 is a keyboard 120 for entering text data and user commands, and a pointing device 122 (such as a mouse) for processing objects displayed on monitor 118. In some embodiments, objects displayed on monitor 118 may be elements of a graphical user interface.

Computer 116 may include a computer-readable memory medium such as a rotating disk 124 for storing readable data. Besides other programs, disk 124 can store application programs including web browsers by which computer 116 connects to a network and the systems described below, according to one aspect of the present disclosure. In some embodiments, disk 124 may be a disk system with a plurality of disk drives arranged as a Redundant Array of Inexpensive Drives (RAID) system, accessible using a storage adapter (not shown).

Computer 116 can also access a computer-readable storage device 126 with removable storage media storing data files, application program files, and computer executable process steps embodying the present invention or the like. For example, the storage device 126 may be a CD-ROM or a DVD ROM. In some embodiments, the storage device 126 may support removable storage media that is read-only device (R), write once-read many (WORM), or rewriteable (RW) type. In some embodiments, the storage device 126 may also be provided with computer 116 to access application program files, audio files and data files stored on a removable storage media. In some embodiments, the removable storage media may be optical, magnetic, magneto-optic, or semiconductor based recording media.

A modem, an integrated services digital network (ISDN) connection, wireless or the like also provides computer 116 with a DSL/Cable/satellite/wireless (or Internet connection) 128 to the World Wide Web (WWW). Internet connection 128 allows computer 116 to send and receive commands, data files, audio files, application program files and computer-executable process steps embodying the present invention.

Computer 116 is also provided with external audio speakers 130A and 130B to assist a listener to listen to music either downloaded on-line from the Internet or off-line using a storage medium. It is noteworthy that a listener may use headphones instead of audio speakers 130A and 130B to listen to music.

FIG. 2 is a block diagram showing the internal functional architecture of computer 116. As shown in FIG. 2, computer 116 includes a central processing unit (CPU) 201 for executing computer-executable process steps and interfaces with a computer bus 208. Also shown in FIG. 2 are a WWW interface 202, a display device interface 203, a keyboard interface 204, a pointing device interface 205, an audio interface 209, video interface 210, printer interface 212, and a disk 124. Audio Interface 209 allows a listener to listen to music, Online (downloaded using the Internet or a private network) or offline (using a CD).

As described above, disk 124 may store operating system program files, application program files, web browsers, and other files. Some of these files are stored on disk 124 using an installation program. For example, CPU 201 executes computer-executable process steps of an installation program so that CPU 201 can properly execute the application program.

A random access main memory (“RAM”) 206 also interfaces to computer bus 208 to provide CPU 201 with access to memory storage. When executing stored computer-executable process steps from disk 124 (or other storage device such as storage device 126 or Internet connection 128), CPU 201 stores and executes the process steps out of RAM 206.

Read only memory (“ROM”) 207 is provided to store invariant instruction sequences such as start-up instruction sequences or basic input/output operating system (BIOS) sequences for operation of keyboard 120.

FIG. 2A shows yet another example of the overall system 100, according to one aspect of the present disclosure. In FIG. 2A, server 110 is shown as a web server and database 114 is shown as a SQL (structured query language) database. FIG. 2A also shows the various steps that are used to access database 114. In step 1, a user using a browser running on the user system 102, submits a URL, as a request. URL includes the application to which the request is directed. In step 2, the web server 110 captures the request, and locates and executes corresponding application specific program (ASP) code for the application to which the request was directed.

In step 3, during execution of ASP code, a structured query language (SQL) code is generated and executed. Based on the executed SQL code, database 114 is accessed to fetch, add, modify or delete information in the database.

In step 4, the results are sent to the web server 110. The web server 110 constructs a response, for example as a HTML code. In step 5, HTML code is sent to the user system 102. In step 6, the HTML page is sent to the browser and displayed on the user system 102.

Although in this example, a user submits a URL as a request, in some embodiments, a user application executing on the user computer 102 may submit a request. The request may be submitted as a URL, and the user application may be configured to receive the response to the request.

Now, an exemplary coupling of the computer 116 to a server over the internet will be described with reference to FIG. 3. FIG. 3 shows an exemplary topology of a computer network with computers similar to computer 116, connected to the Internet 302. For illustration purposes, three computers X, Y and Z are shown connected to the Internet 302 via Web interface 202 through a gateway 301, where gateway 301 can interface N number of computers. Gateway 301 may be similar to gateway 104 with an anonymization system 112. Web interface 202 may be a modem, network interface card or a unit for providing connectivity to other computer systems over a network using protocols such as X.25, Ethernet or TCP/IP, or any device that allows, directly or indirectly, computer-to-computer communications. Gateway 301 and computers X, Y and Z may be located or controlled within a user controlled environment. This user controlled environment may be within the user controlled enterprise or intranet. For convenience, gateway 301 and computers X, Y and Z are grouped together and referred to as user cloud 309. Exemplary topology of a computer network may have additional groups of computers with gateway to define additional user clouds. In one embodiment, data flow outside the user cloud may need special handling.

It is noteworthy that the invention is not limited to a particular number of computers. Any number of computers can be connected to the Internet 302 or any other computer network may be used.

FIG. 3 further shows a second gateway 303 that connects a network of web servers 304 and 305 to the Internet 302. Web servers 304 and 305 may be connected with each other over a computer network. Web servers 304 and 305 receive requests from the user computer and respond to the requests received from the user computer. Web server 304 is coupled to a database 306 and web server 305 is coupled to a database 307. In one embodiment, the web server may be hosting an application for use by the user computer. As an example, web server 304 is hosting server application SA0 and web server 305 is hosting server application SA1. As one skilled in the art appreciates, server applications may be a hosted customer relationship management software (CRM) application, a website, online shop, news service, search applications, social networking applications, blog sites, webmail and the like.

In one embodiment, the gateway 303, servers 304 and 305, and databases 306 and 307 may be hosted at a third party location. For convenience, gateway 303, servers 304 and 305, and databases 306 and 307 are grouped together and referred to as hosted cloud 310. Exemplary topology of a computer network may have additional groups of servers with gateways and databases to define additional hosted clouds.

The following provides a brief description of the Internet. The Internet connects thousands of computers world wide through well-known protocols, for example, Transmission Control Protocol (TCP)/Internet Protocol (IP), into a vast network. Information on the Internet is stored world wide as computer files, mostly written in the Hypertext Mark Up Language (“HTML”). Other mark up languages, e.g., Extensible Markup Language (“XML”) as published by W3C Consortium, Version 1, Second Edition, October 2000, ©W3C may also be used. The collection of all such publicly available computer files is known as the World Wide Web (WWW). The WWW is a multimedia-enabled hypertext system used for navigating the Internet and is made up of hundreds of thousands of web pages with images and text and video files, which can be displayed on a computer monitor. Each web page can have connections to other pages, which may be located on any computer connected to the Internet.

A typical Internet user uses a client program called a “Web Browser” to connect to the Internet. A web browser is a software program that allows users to access the content stored in Web sites. Modern Web browsers can also create content “on the fly”, according to instructions received from a Web site. This concept is commonly referred to as “dynamic page generation”. In addition, browsers can commonly send information back to the Web site, thus enabling two-way communication of the user and the Web site. A user can connect to the Internet via a proprietary network, such as America Online, or via an Internet Service Provider, e.g., Earthlink. The web browser may run on any computer connected to the Internet. Currently, various browsers are available of which two prominent browsers are Microsoft Internet Explorer and Mozilla Firefox. The Web Browser receives and sends requests to a web server and acquires information from the WWW. A web server is a program that, upon receipt of a request, sends the requested data to the requesting user. A standard naming convention known as Uniform Resource Locator (“URL”) has been adopted to represent hypermedia links and links to network services. Most files or services can be represented with a URL.

URLs enable Web Browsers to go directly to any file held on any WWW server. Information from the WWW is accessed using well-known protocols, including the Hypertext Transport Protocol (“HTTP”), the Wide Area Information Service (“WAIS”) and the File Transport Protocol (“FTP”), over TCP/IP protocol. The transfer format for standard WWW pages is Hypertext Transfer Protocol (HTTP).

Web domain is an Internet address that provides connection to a Web server. Generally, URLs have three parts: the first part describes the protocol used to access the content pointed to by the URL, the second contains the directory in which the content is located, and the third contains the file that stores the content:

<protocol>:<domain><directory><file>.

For example:

http://www.ciphercloud.com/bios.html

http://www.ciphercloud.com/index.html

http://fn.cnn.com/archives/may2009/pr3.html

ftp://user1.ciphercloud.com/software/pages.zip

Commonly, the <protocol> part may be missing. In that case, modern Web browsers access the URL as if the http://prefix was used. In addition, the <file> part may be missing. In that case, one of the convention calls for the file “index.html” to be fetched. In some cases, the request may be redirected to fetch another default file.

For example, the following are legal variations of the previous example URLs:

www.ciphercloud.com/bios.html

www.ciphercloud.com

fn.cnn.com/archives/may2009/pr3.html

ftp://user1.ciphercloud.com/software/pages.zip Web Page.

Web page is the content associated with a URL. In its simplest form, this content is static text, which is stored into a text file indicated by the URL. However, very often, the content contains multi-media elements (e.g. images, audio, video, etc) as well as non-static text or other elements (e.g. news tickers, frames, scripts, streaming graphics, etc). Very often, more than one file forms a Web page. However, there is only one file that is associated with the URL and which initiates or guides the Web page generation.

When a Web browser receives an URL, the Web browser requests a Domain Name System (DNS) name server to decode the domain name and return the IP address for the domain name. The DNS name server returns the IP address of the domain name as stored in the DNS name server to the web browser. Web browser uses the IP address for the domain name and sends a request corresponding to the requested URL that confirms to the HTTP protocol to the IP address. In one embodiment, the request is sent using TCP/IP protocol over the internet.

In one embodiment, the user computer 116 sends requests to the server using HTTP protocol. As previously described with reference to FIG. 2A, the request is processed by the web server and a response is sent to the user computer. In the exemplary topology described with reference to FIG. 3, the request is sent over the internet to the server. In some embodiment, requests may be sent using Hypertext Transfer Protocol Secure (HTTPS) protocol, which is a combination of HTTP protocol with SSL/TLS protocol to provide encrypted communication and secure identification of a network web server.

Now, one embodiment of an anonymization system 402 is described with reference to FIG. 4. Anonymization system 402 may be similar to anonymization system 112. FIG. 4 shows the anonymization system 402 as part of gateway 400 that connects to the network, but the anonymization system 402 can be provided in other ways, such as software running on a server, distributed software, or various software and hardware packages operating together. In one embodiment, the anonymization system 402 may be part of the gateway described with reference to FIG. 1A and FIG. 3. The gateway 400 is configured to communicate with the user systems within the user cloud and communicate with the hosted cloud over the network. The anonymization system 402 will be described in detail, with reference to FIG. 4A.

Now referring to FIG. 4A, the anonymization system 402 includes a management console module 404, an anonymization strategy module 406, anonymization module 408, de-anonymization module 410, tokenization module 412 and one or more crypto modules, shown as crypto 1 module 416, crypto 2 module 418 and crypto N module 420. Additionally, a token vault 414 is also part of the anonymization system 402. As one skilled in the art appreciates, various modules referenced in this disclosure may be implemented in hardware, software executed on a computing device or a combination of hardware and software. Additionally, various modules may be implemented as an integrated unit or may be implemented as separate functional units that interact with each other using a communication protocol.

The management console module 404 is configured to communicate with various applications running on the hosted cloud, over the network. Additionally, the management console module 404 is also configured to communicate with the user computer. For example, the management console module 404 may send queries to various applications running on the hosted cloud and receive meta data (or data schema, data definitions) of each application.

The management console module 404 may also present the received meta data information about the applications running on the hosted cloud to the user. The management console module 404 may additionally present the available anonymization strategies for each data field of the meta data to the user computer. The user may select one or more of the anonymization strategies to be applied to various data fields of the application, using the user computer.

The selected anonymization strategy is stored by the management console module 404 in the anonymization strategy module 406, for later use by other modules of the anonymization system 402. The anonymization strategy may be stored as a table in the anonymization strategy module 406. The anaonymization strategy in one embodiment may include schema definition for data manipulation. An exemplary selected anonymization strategy will be described later with reference to FIG. 5.

The anonymization module 408 is configured to intercept any data to be transmitted from a user computer to the hosted cloud. The anonymization module 408 is also configured to communicate with the anonymization strategy module 406 and evaluate various fields of data to be transmitted against anonymization strategy stored in the anonymization strategy module 406. Based upon this evaluation, the anonymization module 408 is configured to perform anonymization of one or more data fields using one or more of the tokenization module 412 and crypto modules, for example, crypto 1 module 416 and crypto 2 module 418 and generate corresponding anonymized data field.

The anonymization module 408 is also configured to reassemble the data to be transmitted to the hosted cloud, using the anonymized data fields. In one embodiment, the reassembled data includes one or more unique data pattern added to the anonymized data fields to indicate the selected anonymization strategy used to anonymize the data fields. In one embodiment, a preamble is appended to the beginning of the anonymized data field. In one embodiment, a postamble is appended to the end of the anonymized data field. The reassembled data is forwarded to the hosted cloud over the network using the gateway 400. Various functions and features of the tokenization module 412 and crypto modules will be described later.

The de-anonymization module 410 is configured to intercept any data received by the user computer from the hosted cloud. The de-anonymization module 408 is also configured to evaluate various fields of data received from the hosted cloud to detect and decode the preamble and postamble of the data fields. Based upon this evaluation, the de-anonymization module 410 is configured to perform de-anonymization of one or more data fields using one or more of the tokenization module 412 and crypto modules, for example, crypto 1 module 416 and crypto 2 module 418 and generate corresponding de-anonymized data field. The de-anonymization module 410 is also configured to reassemble the data to be transmitted to the user computer, using the de-anonymized data fields. The reassembled data is forwarded to the user computer.

Now, referring to FIG. 5, an exemplary anonymization strategy data table 500 for server application SA0 stored by the anonymization strategy module 406 is described. As one skilled in the art appreciates, the anonymization strategy data may be stored in other forms other than a table form. Column 502 shows various data fields of server application SA0. Each of the rows of table 500 shows the data field and their corresponding characteristics. As an example, Column 504 shows data type for each of the data fields, for example numeric, alphabetical or alpha-numeric characters. Column 506 shows the length of the data field. Column 508 shows whether any portion of the data field needs to be retained as originally provided by the user computer. Column 510 shows the selected anonymization strategy for each of the data fields.

Now referring to row 512, various attributes of DATA FIELD 1 is stored in table 500. For example, DATA FIELD 1 is a numeric field of length 10 characters. Characters 8 through 10 of DATA FIELD 1 needs to be retained in its original form. The selected anonymization strategy for DATA FIELD 1 is TPF1. For example, DATA FIELD 1 may be a telephone number and characters 10:08 may represent the area code of the telephone number and may have to be maintained in its original form at the hosted cloud. However, characters 07:01 will be anonymized using anonymization strategy TPF1. In one embodiment, TPF1 may correspond to anonymization using tokenization module 412. In one embodiment, TPF1 may correspond to anonymization using Crypo 1 module 416. In one embodiment, a preamble indicative of TPF1 anonymization strategy may be appended to the anonymized DATA FIELD 1.

Now referring to row 514, various attributes of DATA FIELD 4 is stored in table 500. For example, DATA FIELD 4 is an alphabetic field of length 8 characters. No characters need to be retained in its original form. The selected anonymization strategy for DATA FIELD 4 is TRF4. In one embodiment, TRF4 may correspond to anonymization using crypto 2 module 418. In one embodiment, TRF4 may correspond to anonymization using crypto N module 420. In one embodiment, a preamble indicative of TRF4 anonymization strategy may be appended to the anonymized DATA FIELD 4.

Now referring to row 516, various attributes of DATA FIELD 8 is stored in table 500. For example, DATA FIELD 8 is an alpha-numeric field of length 24 characters. No characters need to be retained in its original form. The selected anonymization strategy for DATA FIELD 8 is none. Based on this selection, no anonymization of DATA FIELD 8 will be performed and the data contained in DATA FIELD 8 will be sent in its original form.

Now various anonymization techniques implemented in various anonymization modules like tokenization module 412 and crypto modules like crypto 1 module, crypto 2 module and the like will be described. The anonymization technique selected for a data field may be based upon multiple factors. One of the factors is level of desired security. One of the other factors is data attribute preservation for the data field. The data attribute preservation refers to retaining capabilities to perform operations on the stored data field in their anonymized form as compared to the stored data in its original or clear form. Some of the operations that may be performed on the stored data field in their anonymized form include sorting in the order of their clear data form (or original data form), searching, searching regardless of the case of the characters, searching using partial string match or wild card match. As one skilled in the art appreciates, as more data attributes are preserved in the stored data form, in some embodiments, it may lead to decreased level of data security. Clear data or original data refers to the data as present in the data field before anonymization.

Anonymization techniques may be broadly divided into two categories. One, a token based anonymization. The token based anonymization may be implemented in the tokenization module 412 and may require local storage of the tokens in the token vault 414. Another technique is to use a symmetric key encryption based anonymization. Symmetric key encryption based anonymization technique may be implemented in one or more of the crypto modules, for example, crypto module 1, crypto module 2 and the like. Now, various anonymization techniques for each of the token based anonymization and symmetric key encryption based techniques will be described.

Token Based Anonymization Techniques:

Random token anonymization: In this technique, for data contained in each data field, a corresponding token is created. The token vault 414 is used to store the relationship between the original or clear data and the corresponding token. The token is sent to the hosted cloud, after prepending a preamble indicative of the type of anonymization performed on the data field. When the data field is retrieved from the hosted cloud, based on the preamble indicative of the type of anonymization performed, the token is used to extract the corresponding clear data stored in the token vault 414. The de-anonymized data is then forwarded to the user computer.

Sortable token anonymization: In this technique, the token vault is organized in a balanced, ordered tree, with each node representing a specific token that corresponds to a specific clear data. For an input data, the tokenization module 412 looks up the tree in the token vault 414 for a node that has not been used for clear data corresponding to the input data and establish a new token that corresponds to the input data. The new token is mapped to the input data. The new token in the selected node maintains the order of the tree. In this way, the token continues to maintain the order that corresponds to the original or clear data. The order may correspond to the position, sequence, or value of the original data. For example, the order may be based on alphabetical sequence, numerical sequence, value of the characters based on an assigned value or value of the characters based on a pre-assigned value, for example, Unicode value. The new token is then sent to the hosted cloud.

Token anonymization that allows case-insensitive search: In one embodiment, for each data, two tokens are generated. One, based on the original or clear form of the data and the other based on all lower cases. In one embodiment, random token anonymization technique is used, as described above. Both of the generated tokens are sent to the hosted cloud, with the token corresponding to all lower case is stored in a hidden field in the application.

When a search request is received for this field from the user computer, for example, as a keyword in clear form, search request is generated by the anonymization system 402, for both the clear form and all lower case. For example, anonymized keywords are generated, one based on the keyword in its original form and another based on keyword with all lower cases. The search request is then sent to the host cloud, with anonymized keyword for both clear form and all lower case. In one embodiment, the search request is sent with an “OR” logic so as to receive search results for both the keywords. Any results of the search received from the host cloud for both the anonymized keyword for clear form and anonymized keyword for all lower case will then be de-anonymized by the de-anomymization module of the anonymization system. The de-anonymized results are then presented to the user computer. As one skilled in the art appreciates, a keyword received in a search request may be full data field, few characters of a data field or few characters with a wild card. Principles described herein will work for all of these cases, as the search request sent to the host cloud will be based on anonymized keyword.

Searchable encryption: In this technique, encryption algorithm is designed to encrypt words of text. The encrypted values preserve the properties of sorting and equality of the clear values. As an example, the equality can be either case-sensitive or case-insensitive. As another example, sorting may be either case-sensitive or case-insensitive. In one embodiment, accented characters may be treated as equal characters. i.e. a character “E” and “É” may be treated as equal or same. In one embodiment, accented characters may be treated as adjacent characters, with “É” positioned after “E”. Some of these techniques may permit both order preserving property for the anonymized data and case-insensitive search capability. Exemplary searchable encryption to encode words of a text will now be described with reference to FIGS. 6A-6B.

FIG. 6A shows an exemplary block diagram of a searchable encryption system 600. The searchable encryption system 600 may be implemented in the anonymization system 402. The searchable encryption system 600 includes an input logic 602 to receive input word to be encrypted, an encryption engine 604, a compare logic 606 and an encode logic 608. Various elements of the encryption system 600 may be implemented in various functional modules of the anonymization system 402, for example, in the anonymization module 408, crypto modules 416-418, de-anonymization module 410 and the like. For example, the encryption engine 604 may be part of one of the crypto modules 416-420.

The input logic 602 is configured to receive the input word to be fed to the encryption engine 604. In one embodiment, the encryption engine 604 receives each of the characters of the input word and generate a random character as an output. The encryption engine 604 may include a Pseudo-Random Number Generation Algorithm (PRNG) to generate a random character as an output. The PRNG is preferably secure and is fast. One exemplary PRNG may be a RC4 cipher. In one embodiment, the generated random character will be within a predefined range of characters, which narrows in subsequent iterations, as will be described later.

The output of the encryption engine 604 is fed to the compare logic 606. The compare logic 606 is also configured to receive the corresponding input character as an input. The compare logic 606 is configured to compare generated output character of the encryption engine 604 with the corresponding input character in a first range of characters and generate a binary bit, based on whether the input character is higher in position or lower in position relative to the generated output character. As an example, a bit 0 is generated if the position is lower and a bit 1 is generated, if the position is higher relative to the generated output character. The generated output character is now fed as input to the encryption engine 604 to generate a second generated output character, within a second range of characters.

The second generated output character is again compared with the input character by the compare logic 606 and generate next bit of output based on the position of the input character relative to the second generated output character, within the second range of characters (which is smaller than the first range). The operation is repeated to generate additional output bits until the range is such that there are only two characters before or after the input character. Now, the next character of the input word is processed to generate second set of output bits. This operation will be further described with reference to FIG. 6B with table 620.

Referring to FIG. 6B, table 620 shows for each iteration in column 622, the corresponding character range in column 624 and the output of the compare logic for each iteration in column 626. For simplicity, only words with uppercase characters will be described. For example, if the word to be encoded is “GOLD”, initial encoding starts with encoding the first character “G”. The character G is fed to the encryption logic 604 as a first input character and the encryption logic 604 is configured to generate a random character as an output, for example, a random character between A-Z, which defines the first range of characters. Let us assume, the first generated random character output is Q.

Now, the first input character “G” is compared with the first generated random character output “Q” by the compare logic 606 to see if it is higher in position or lower in position relative to the first generated random character output, within the range shown in cell 628 which corresponds to the first iteration 630. In this case, as character G is lower in position to the first generated random character output Q, a binary value of “0” is ouput as the first bit of the encoded data, as shown in cell 632. Additionally, the first generated random character output Q defines the upper bound (as character Q is higher in position relative to character G) for the range of characters to be used in the next iteration, for example, second range of characters A-Q.

Next, the first generated random character output (in this case character “Q”) is input to the encryption logic 604 and the encryption logic outputs a second generated random character output, within the second range of characters, as shown in cell 634. As one skilled in the art appreciates, the second range of characters is A-Q and the range of characters the encryption logic can output will be configured to be between A-Q. Let us assume, the second generated random character is character “C”.

Now, the first input character is again compared with the second generated random character output to see if it is higher in position or lower in position relative to the second generated random character output. In this case, as character G is higher in position to the second generated random character output C, a binary value of “1” is output as the second bit of the encoded data, as shown in cell 636. Additionally, the second generated random character output C defines the lower bound (as character C is lower in position relative to character G) for the range of characters to be used in the next iteration, for example, third range of characters C-Q.

Now, the second generated random character output C is input to the encryption engine 604 and the encryption engine 604 outputs a third generated random character output. In this case, the third range of characters the encryption engine 604 can output is be configured to be within the third range of characters C-Q. Let us assume, the third generated random character is character “E”, with a “1” assigned to the third bit of the encoded data, based on the comparison. Additionally, the third generated random character output E defines the lower bound (as character E is lower in position relative to character G) for the range of characters to be used in the next iteration, for example, fourth range of characters E-Q.

Similarly, referring to iteration 4, fourth generated random character is character “J” with an output of “0” as the forth bit of the encoded data, based on the comparison. Additionally, the fourth generated random character output J defines the upper bound (as character J is higher in position relative to character G) for the range of characters to be used in the next iteration, for example, fifth range of characters E-J.

Similarly, referring to iteration 5, the fifth generated random character is character “H”, with a “0” assigned to the fifth bit of the encoded data, based on the comparison. This process is repeated until there are two or less characters left before and after the first input character, at which time, the encoding stops. So, in this case, as there are only two characters E and F before and one character J after the first input character G, the encoding stops. So, the final encoded output for character “G” is “00110”.

The rest of the characters “O”, “L” and “D” are similarly processed to get their corresponding encoded output. As an example, let the final encoded output for “GOLD” is 010010001010010100000110. As one skilled in the art appreciates, depending upon the random character generated by the encryption engine 604, each character may have different number of bits as their encoded output. Now the final encoded output is transformed into a character representation, using an encode logic 608, which will be described now.

In one embodiment, the character representation of the final encoded output is done by splitting the final encoded output into a plurality of bits of data and assigning a unique character to each unique combination of a plurality of bits. As an example, four bits may be used to represent 16 different unique combinations, with each combination assigned a specific character. For example, a base-16 encoding may be used and sixteen characters, characters B-Q assigned to the base-16 encoded data. As an example, 0000 may be encoded as character B and 1111 may be encoded as character Q. With base-16 encoding, other characters may be available for assignment for special purposes or for padding. Other random assignments of characters to the base-16 encoded data are also possible. In some embodiments, dummy characters may be padded in the beginning and the end of the range, so that a lowest character in the character range will not encode as all zeros and highest character in the character range will not encode as all ones.

In one embodiment, in order to provide case insensitive search capability, the encoded output is mapped into lower-case characters, for example, characters b-q. Then, specific characters of the mapped word is capitalized, based on the capitalization of the original input word. For example, if the input word was Mom, and the final output is mapped into character combination “def”, then, based on the capitalization of the original input word, the mapped character combination is modified to be “Def”. A case sensitive and a case insensitive search on “mom” would return the appropriate results.

In some situations, the final encoded output may result in a mapped word that may have less characters than the original input word. As an example, for a three character input word, the encoded output may only generate two mapped characters. In such a situation, at least one character that is not used in the output mapping (as only 16 characters are used in a base-16 encoding) may be used as a padding character. Preferably, the padding character should sort before all other used characters. As an example, if the characters B-Q are used for mapping, character A may be used as a padding character to be added to the mapped characters. Character A would sort before the characters B-Q.

As an example, if the input of Mom mapped into “De”, then with the added padding, the modified mapped characters will be Dea, which will have the same number of characters as the original input character. If the original input was “moM” and the input mapped into “De”, then the modified mapped characters will be “deA”, which preserves both the number of characters of the original input and the case of the characters of the original input.

In the above examples, only characters A-Z were considered. In order to expand the search and sortable encryption capabilities to characters outside the A-Z range, one or more escape characters may be pre-pended and/or post-pended to the input word. The escape character may indicate the characteristics of the word being encrypted. As an example, unique escape characters may be pre-pended to the input word to indicate if the input word is a character less than the number “0”, numbers between 0-9, numbers greater than 9 but less than A, lettes A-Z, single byte character greater than “Z”, Unicode character in the range U+0080 to U+00FF, Unicode character in the range U+0100 to U+FFFF, Unicode character in the range U+10000 to U+10FFFF and the like. Additionally, a unique escape character may be post-pended to the input word to indicate “end of word”.

As one skilled in the art appreciates, in one embodiment, the searchable encryption system 600 may additionally have a decode logic 612 to receive the encoded word 614 and output the decoded word character string. The decode logic 612 would receive the encoded characters of the encoded word as an input, perform the manipulation necessary to decode the encoded characters one character at a time and reassemble the encoded word as a decoded word. An exemplary decoding operation will be further described with reference to FIG. 6C with table 650.

Referring to FIG. 6C, table 650 shows for each iteration in column 652, the input bit 654 from the encoded word, corresponding character range in column 656. For simplicity, only decoding of character “G” of the encoded word will be described. For example, the character “G” was encoded as “00110”. Iteration 0 refers to the initial set up of the range of characters, for example, first range of characters 658, which will be from A-Z. In iteration 1, the input bit is “0” and the input bit “0” is fed to the decode logic 612. The decode logic 612 generates first random range character Q as an output, based on the input bit of “0”. Since input bit was a “0”, character Q defines the upper bound for the next range of characters to be used in the decoding process. Now, the second range of characters 660 is defined to be from A-Q, based on the generated first random range character “Q”.

Next, in iteration 2, next input bit of “1” is input to the decode logic 612. The decode logic 612 generates second random range character C as an output, based on the input bit of “1”. As the input bit was a “1”, character C+1 defines the lower bound for the next range of characters to be used in the decoding process. Now, the third range of characters 662 is defined to be from D-Q, based on the generated second random range character “C”.

Next, in iteration 3, next input bit of “1” is input to the decode logic 612. The decode logic 612 generates third random range character E as an output, based on the input bit of “1”. As the input bit was a “1”, character E+1 defines the lower bound for the next range of characters to be used in the decoding process. Now, the fourth range of characters 664 is defined to be from F-Q, based on the generated third random range character “E”.

Next, in iteration 4, next input bit of “0” is input to the decode logic 612. The decode logic 612 generates fourth random range character J as an output, based on the input bit of “0”. As the input bit was a “0”, character “J” defines the upper bound for the next range of characters to be used in the decoding process. Now, the fifth range of characters 666 is defined to be from F-J, based on the generated fourth random range character “J”.

Next, in iteration 5, next input bit of “0” is input to the decode logic 612. The decode logic 612 generates fifth random range character H as an output, based on the input bit of “0”. As the input bit was a “0”, character “H” defines the upper bound for the next range of characters to be used in the decoding process. Now, the sixth range of characters 668 is defined to be from F-H, based on the generated fifth random range character “H”.

Now, the fifth range of characters 668 is narrowed down to three characters, F, G and H. The middle character “G” is chosen as the decoded character for the encoded input word. Similarly, rest of the encoded input words are decoded to generate the original word “GOLD”.

Scalable Order Preserving Tokenization:

As previously discussed with reference, various tokenization mechanisms like sortable token anonymization build an ordered tree. As one skilled in the art appreciates, in order to perform the anonymization or de-anonymization, the tree has to be searched and/or modified. As an example, either to locate a position in the tree to insert a new node representative of a data contained in a data field or to locate a node representative of a data contained in a data field. As the number of tokens grow, the number of nodes representing a data increases and the tree representing various data becomes larger. As one skilled in the art appreciates, larger tree poses burden on computing resources. In some embodiments, it is preferable to quickly identify a node of interest, for search, sort or insert operations. One or more embodiments of implementing a system and method to create a tree of tokens will be described with reference to FIGS. 7A-7D.

Now, referring to FIG. 7A, an exemplary tree 700 to generate tokens for various data in a data field is described. Tree 700 has plurality of nodes. These nodes are directly or indirectly related to each other. As an example, nodes may be referred to as root node, child node, parent node, based on their relationship with other nodes. Root node can have up to two child nodes. Each child node may have up to two child nodes and one parent node. For example, each node may have a left child node and a right child node.

For example, Tree 700 has a root node 702 and a plurality of child nodes 704-716. Each child node connects to a parent node. For example, the parent node for child node 706 is root node 702. The parent node for node 710 is node 704. Node 704 is the left child node of parent node 702. Node 706 is the right child node of parent note 702. Each node represents the corresponding character/s depicted in the node. For example, node 702 represents character “b”, node 710 represents character “aa” and the like.

Initially, the tree 700 starts with just the root node 702, representing character “b”. Then, the tree 700 is built, to represent more characters. Now, selection of a left child node or a right child node to represent a character is described. In one embodiment, the left child node represents a value less than the value of the parent node and the right child node represents a value greater than the value of the parent node. As an example, for node 702 representing a value of “b”, a left child node 704 may represent any value less than “b”, (for example, “negative infinity” to “a”) and the right child node 706 may represent any value greater than “b” (for example, “c” to “infinity”). In our specific example, the left child node 704 is representing a value of “a” and right child node 706 is representing a value “c”. In some embodiments, a left child node may represent a binary value of “0” and the right child node may represent a binary value of “1” to generate a corresponding token value for the data represented by a node. This will be explained further with reference to FIG. 7B.

Now, referring to FIG. 7B, an exemplary encoding of data represented by a node is described using table 730. Referring to table 730, column 732 shows the nodes, column 734 shows the data value and column 736 shows the token value. As an example, referring to row 738, the node 702 represents data value “b” and the corresponding token for data value “b” is “1”. In this example, the last value in the token represents an end marker for the token. The end marker in this example, is a “1”. As node 702 is the root node, the token value for node 702 is just the end marker.

Now, referring to row 740, the node 712 represents data value “bc” and the corresponding token value for the corresponding data value “bc” is 011. The token value for data value “bc” is derived based on the path followed to reach node 740 from the root node 702, with a left child node in the path representing a “0” and a right child node in the path representing a “1”.

Now, referring back to FIG. 7A, the path followed from root node 702 to reach child node 712 is root node 702—right child node 706—left child node 712. Now, encoding a “0” for a left child node and a “1” for a right child node and adding an end marker of “1”, we get the corresponding token value of “011” for node 712 representing a data value of “bc”

As one skilled in the art appreciates, new nodes are added as a left child node or a right child node to a parent node, based on the data value the node represents. As an example, new data value “0” is yet to be represented in the tree 700. So the system starts with the root node 702 and compares the new data value “0” with the data value for node 702 “b”. As the new data value is less than the data value for node 702, new data value will be represented by a left child node to the root node 702. Now, the data value for the next left child node is compared with the new data value. If the tree 700 did not have a left child node, so there was an open node space (i.e. node 704 did not exist), a new left child node will be added to the root node 702 at the open node space to represent the new data value “0”.

As the tree 700 does have a left child node 704, the value of left child node 704 is compared with the new data value “0”. The new data value “0” is less than the data value “a” for the left child node 704. So the system continues to search for an open node space. The open node space has to be a left child node location, if the new data value is less than the node value for the corresponding parent node. The open node space has to be a right child node location, if the new data value is greater than the node value for the corresponding parent node. So, the search for an appropriate open node space for the new data value continues, by comparing the new data value with corresponding nodes in the tree 700.

Now, as the new data value “0” is less than the data value in node 704, if the left child node of node 704 is a candidate to insert a new node. However, there is already a node 708. The data value of node 708 is ‘1”. The new data value “0” is less than the data value of node 708. So, the left child node of node 708 is a candidate to insert a new node. Currently, there is no left child node for node 708 and so, there is an open left node space. A new node 718 is inserted in the tree 700, with node 718 representing a data value of “0”. As one skilled in the art appreciates, the corresponding token value for data value of “0” will be 0001.

In one embodiment, select limbs of the tree 700 may be divided and stored as separate limbs of nodes. Separate limbs of nodes may be stored in different systems. As an example, nodes 704, 708, 710 and 716 may form one limb of nodes. As another example, nodes 706, 712 and 714 may form another limb of nodes.

In one embodiment, the tree of nodes may be divided into a plurality of sub trees, with each sub trees having a sub parent node and their corresponding child nodes. For example, a plurality of sub tree may be created for each of the alphabets ‘a” to ‘z”, with nodes representing data values “a”, “b”, . . . “z” each representing a sub parent node. Each of the sub trees will represent a range of values. The range of values will be based on the value of the sub parent node. In one embodiment, the plurality of sub trees may be distributed amongst a plurality of systems. Based on the value of the data to be searched for an existing token or generate a new token to be added, one of the plurality of the sub trees may be selected. For example, value of the data is compared with the value of data represented by a sub parent node and appropriate sub tree is selected.

In yet another embodiment, for each tree node, their corresponding node attributes are stored. The attributes may be node data value, left boundary value, right boundary value, left child node value, right child node value and token value (or path to the node). For example, a tuple with these attributes may be stored in the anonymization system. As an example, referring to FIG. 7C, table 750, for some of the tree nodes (shown in column 752) for tree 700, their corresponding attributes are stored: node data value (column 754), left boundary value (column 756), right boundary value (column 758), left child node value (column 760), right child node value (column 762), token value (764). For example, referring to row 768, for tree node 704, the corresponding node data value is “a”, left boundary value is “negative infinity”, right boundary value is “b”, left child node value is “1”, right child node value is “aa” and token value is “01”. As another example, referring to row 770, for tree node 708, the corresponding node data value is “1”, left boundary value is “negative infinity”, right boundary value is “a”, left child node value is “null” (or blank), right child node value is “2” and token value is “01”. As one skilled in the art appreciates, referring to row 770, the node 708 has a open left child node space, indicated by the “null” value.

Now, an exemplary tuple for each of the node may be represented as follows:

tuple: [node data value, left boundary value, right boundary value, left child node value, right child node value, token value]

Now, for example, in order to search for a token for data value of “0” following query may be sent to retrieve tuple that corresponds to appropriate node.

select tuple from tokens

where (x> left boundary value) and (x< right boundary value) and ((node data value=x) or ((node data value>x and left child node value=null) or (node data value<x and right child node value=null))

where in this example, x=data value of “0”.

Now, referring to table 750, in response to this query, the system would return the tuple corresponding to node 708, as it meets all the criterion identified in the query. More specifically, the tuple corresponding to node 708 satisfies the expression (node data value>x and left child node value=null). Further, node data value for node 708 is 1 and not equal to “0”. So, the system has to insert a new node to represent the data value of “0”.

Based on the returned tuple, the system can determine that new node representing the data value “0” will be a child node of node 708. As the data value “0” is less than the data value represented by node 708 (which is “1”), the new node will be a left child node. The corresponding token value for data value “0” can be derived from the token value for node 708 (which is 001), by the addition of a “0” before the end marker “1”. So, the token value for data value “0” will be “0001”.

Now, referring to FIG. 7D, in table 750, a new node 718 is added to the tree 700 and corresponding attributes of the new node 718 will be stored. For example, referring to row 770, the tuple representing the attributes of the new node 718 will be: node data value=“0”, left boundary value=“negative infinity”, right boundary value=1, left child node value=null, right child node value=null, token value=0001. Additionally, referring to row 772, the value of left child node value for node 708 will be updated with a value of “0”.

Now, after the insertion of new node 718, if another query is executed to search for a token for data value of “0”, as shown below, then the tuple corresponding to the node 718 will be returned instead of tuple corresponding to the node 708. The exemplary query will be:

select tuple from tokens

where (x> left boundary value) and (x< right boundary value) and ((node data value=x) or ((node data value>x and left child node value=null) or (node data value<x and right child node value=null)))

where in this example, x=data value of “0”

As one skilled in the art appreciates, tuple corresponding to node 708 no longer meets the conditions of the query, as the value of left child node value for node 708 has been updated with a value of “0”, the data value of new node 718.

As one skilled in the art, the data representing the tree and data corresponding to the nodes of the tree may be stored in the token vault 414. As described previously, the tree may be split in one or more ways and stored in a plurality of token vault 414. In some embodiments, portions of the token vault 414 may be distributed amongst multiple systems. In some embodiments, these systems may share some of the computing resources. In some embodiments, these systems may have independent resources. In some embodiments, these systems may be networked systems. In some embodiments, these systems may be a cluster of plurality of systems.

Now referring to FIGS. 8A-8C and FIGS. 9A-9C an exemplary anonymization implementation will be described. Referring to FIG. 8A, a screen shot 800 presented on a user computer to open a new account for an application running on a hosted cloud is shown. More specifically, the data field 802 refers to the Account Name and the corresponding data for the Account Name is entered in the form field 804. For example, the Account Name entered is “Test Account 1”.

FIG. 8B shows the “POST” action data as transmitted to the hosted cloud, without anonymization. More specifically, reference numeral 804-1 shows the post data that corresponds to the Account Name. As one skilled in the art appreciates, it is not difficult to ascertain the Account Name as “Test Account 1”.

FIG. 8C shows the “POST” data as transmitted to the host cloud, with an embodiment of the anonymization technique described in this disclosure. More specifically, reference numeral 804-2 shows the anonymized data that corresponds to the Account Name. FIG. 8D shows various parts of the anonymized data.

Referring to FIG. 8D, the anonymized data 804-2 includes two anonymized data fields 804-21 and 804-22. The first data field 804-21 corresponds to the word “Test” and the second data field 804-22 corresponds to the word “Account”. Additionally, the preamble 806 for the first data field is TPF1. The postamble 808 for the first data field is 1TSF. Similarly, the preamble 810 for the second data field is TPF1 and the postamble 812 for the second data field is 1TSF1.

Now, referring to FIG. 9A, a screen shot 900 presented on a user computer to view accounts set up for an application running on a hosted cloud is shown. More specifically, the screen shot 900 shows the list of account names that have been set up, for example, account name 902 referring to Test Account 1 that is received in response to a GET action initiated by the user computer to the application hosted on the hosted cloud.

FIG. 9B shows the HTML code 904 constructed by the application hosted on the hosted cloud in response to the GET action. More specifically, the anonymized values 906 of the account name is returned in the HTML code 904. The anonymized value 906 corresponds to the anonymized data 904-2.

FIG. 9C shows the HTML code 908 constructed after processing the received HTML code 904 from the hosted cloud and de-anonymize the anomymized data field 906. More specifically, the HTML code 908 includes the de-anonymized value as shown in data field 910. The HTML code 908 with de-anonymized value for Account Name data field is sent to the user computer, in response to the GET action initiated by the user computer.

Now, referring to FIG. 10, a method of anonymization of data to be transmitted to a hosted cloud is described. In block S1002, an anonymization strategy for data anonymization is provided. In one embodiment, the anonymization strategy is stored in a anonymization strategy module 406. In one embodiment, a management console module 404 may query applications running on the hosted cloud and present and receive meta data (or data schema, data definitions) of each application. The management console module 404 may present the available anonymization strategies for each data field of the meta data to the user computer. The user may select one or more of the anonymization strategies to be applied to various data fields of the application.

The selected anonymization strategy is stored by the management console module 404 in the anonymization strategy module 406, for later use by other modules of the anonymization system 402. The anonymization strategy may be stored as a table in the anonymization strategy module 406. The anaonymization strategy in one embodiment may include schema definition for data manipulation. An exemplary selected anonymization strategy is described with reference to FIG. 5.

In block S1004 data to be transmitted to a destination is received. In one embodiment, the data to be transmitted to a destination is received by the anonymizaton system from the user computer.

In block S1006, selective anonymization of data based on anonymization strategy is performed. For example, an anonymization strategy may be selected based on the data field and corresponding anonymization strategy, as shown and described with reference to FIG. 5. An exemplary anonymization of data may be performed, as described with reference to FIGS. 8A-8D.

In block S1008, the anonymized data is transmitted to the destination. In one embodiment, the anonymized data is transmitted to the application running on the hosted cloud. An exemplary transmission of anonymozed data was at least described with reference to FIGS. 8A-8D.

In block S1010, the anonymized data is stored in the destination. For example, the anonymized data may be stored in a database coupled to an application server, as shown in FIG. 3.

Now referring to FIG. 11A, an exemplary method of processing a request at the destination is described. In block S1102, a request for the stored anonymized data is received at the destination. For example, as described with reference to FIGS. 9A-9C, a GET action request is received at the application running on a hosted cloud.

In block S1104, the request is executed at the destination to selectively retrieve stored anonymized data. As an example, the application server running on the hosted cloud processes the GET action and retrieve anonymized data corresponding to the GET action from a user computer.

In block S1106, the selectively retrieved stored anonymized data is returned. As an example, the application running on the hosted cloud assembles a response to the GET action and returns the selectively retrieved stored anonymized data, as shown in FIG. 9B.

Now referring to FIG. 11B, an exemplary method or processing a response to a request is described. In block S1108, a selectively retrieved anonymized data is response to a request is received. As an example, a response from the hosted cloud to a GET action is received by the anonymization system 402. The response may be a HTTP response as shown in FIG. 9B.

In block S1110, the retrieved stored anonymized data is de-anonymized. For example, the retrieved anonymized data is evaluated by the de-anonymization module 410. The selective preamble contained in the anonymized data is identified. The de-anonymization module performs the de-anonymization based on the identified preamble and generates the de-anonymized data. An exemplary de-anonymized data is shown in FIG. 9C.

In block S1112, the de-anonymized data is returned in response to the request. For example, an HTTP response as described with reference to FIG. 9C is returned to the user computer.

As it is apparent to one skilled in the art, data transfers from user applications are selectively anonymized using one or more anonymization techniques, based on selected anonymization strategy. The anonymization of data happens transparently to the user application.

Although in some embodiments, anonymization techniques may have be described with reference to encryption of the input word, these techniques may be adapted and practiced using tokenization as well.

Although in this disclosure reference is made to transmitting anonymized data to a hosted cloud or a destination system located at a third party location, the disclosure is not limited to the disclosed embodiments. In some embodiments, anonymized data may be stored in a user cloud itself. The server application may be any type of application, including word processing applications, mail applications and the like.

Although the hosted cloud has been described with one or more servers and databases, these clouds may be implemented with additional servers and databases that may act as redundant systems or cumulative systems.

As one skilled in the art appreciates, various embodiments of this disclosure may provide one or more of the following benefits. For example, anonymization of transmitted data may occur transparent to the user application. Anonymized data may be sortable, based on the selected anonymization strategy. Anonymized data may be searchable, based on the selected anonymization strategy. Anonymized data may be subjected to case-insensitive search, based on the selected anonymization strategy. Portions of a data field may be anonymized, retaining other portion of the data field in its original form. Portions of the data field retained in its original form may be bypassed from the anonymization process and system, which may lead to improved overall performance of the anonymization system.

While embodiments of the present invention are described above with respect to what is currently considered its preferred embodiments, it is to be understood that the invention is not limited to that described above. To the contrary, the invention is intended to cover various modifications and equivalent arrangements within the spirit and scope of the appended claims. 

What is claimed is:
 1. A method for anonymizing data to be transmitted to a destination computing device, comprising: receiving data to be transmitted to the destination from a user computer, the data including one or more characters; providing a tree of nodes, with each node representing a node value and a corresponding token value, wherein, each node configured to have a left child node and a right child node, with each of the path leading to the left child node and the right child node each representing a value and the values of the paths followed to reach one of the nodes representing the corresponding token value for the one of the nodes; providing a data store to store a plurality of attributes associated with each node, a plurality of attributes including the node value, left boundary value, right boundary value, left child node value, right child node value and the token value; finding a node with a node value equal to a value of the data, by searching the data store for a node having the attributes of (x>left boundary value) and (x<right boundary value) and (node data value=x), where, x=value of the data; selecting the corresponding token value of the found node as the anonymized representation of the data; replacing the data with the token value; and transmitting the token value instead of the data to the destination computing device over a network for storage in the destination computing device.
 2. The method of claim 1, wherein, if a node with a node value equal to the value of the data is not found, adding another node to the tree of nodes representing the value of the data, with a corresponding token value.
 3. The method of claim 2, wherein, adding another node further including locating a node with available child node having the following attributes: (x>left boundary value) and (x<right boundary value) and ((node data value>x and left child node value=null) or (node data value<x and right child node value=null)), where, x=value of the data.
 4. The method of claim 1, wherein the path leading to the left child node and the right child node each representing one of the two binary values.
 5. The method of claim 1, further including receiving anonymized data from the destination computing device; and de-anonymizing the received anonymized data by replacing the token value with data value.
 6. A method for anonymizing data to be transmitted to a destination computing device, comprising: receiving data to be transmitted to the destination from a user computer, the data including one or more characters; replacing the data with a token representative of the data; and transmitting the token to the destination computing device over a network; wherein replacing the data to a corresponding token further including: providing a tree of nodes, with each node representing a node value and a corresponding token value; finding a node with a node value equal to the value of the data; and selecting the corresponding token value as the token representative of the data; wherein, each node configured to have a left child node and a right child node, with each of the path leading to the left child node and the right child node each representing a value; and the values of the paths followed to reach one of the nodes representing the corresponding token value for the one of the nodes; wherein the path leading to the left child node and the right child node each representing one of the two binary values; providing a data store to store a plurality of attributes associated with each node, a plurality of attributes including the node value, left boundary value, right boundary value, left child node value, right child node value and the token value; and wherein, if a node with a node value equal to the value of the data is not found, adding another node to the tree of nodes representing the value of the data, with a corresponding token value, by locating a node with available child node having the following attributes: (x>left boundary value) and (x<right boundary value) and ((node data value>x and left child node value=null) or (node data value<x and right child node value=null)), where, x=value of the data.
 7. The method of claim 6, further including receiving anonymized data from the destination computing device; and de-anonymizing the received anonymized data by replacing the token value with data value.
 8. An anonymization system to anonymize data transmitted to a destination computing device, comprising: a logic to receive data to be transmitted to the destination from a user computer, the data including one or more characters; a data store with a tree of nodes, with each node representing a node value and a corresponding token value, wherein, each node is configured to have a left child node and a right child node, with each of the path leading to the left child node and the right child node each representing a value; wherein, the values of the paths followed to reach one of the nodes represents the corresponding token value for the one of the nodes; and wherein, the data store stores a plurality of attributes associated with each node, a plurality of attributes including the node value, left boundary value, right boundary value, left child node value, right child node value and the token value; a logic to find a node with a node value equal to a value of the data, wherein, a node with a node value equal to the value of the data is found by searching the data store for a node with (x> left boundary value) and (x< right boundary value) and (node data value=x), where, x=value of the data, and select the corresponding token value of the found node as the anonymized representation of the data; and an anonymization module to replace the data with the token; and transmit the token value to the destination computing device over a network for storage in the destination computing device.
 9. The system of claim 8, wherein, if a node with a node value equal to the value of the data is not found, another node is added to the tree of nodes that represents the value of the data, with a corresponding token value.
 10. The system of claim 9, wherein, another node is added by locating a node with available child node having the following attributes: (x>left boundary value) and (x<right boundary value) and ((node data value>x and left child node value=null) or (node data value<x and right child node value=null)), where, x=value of the data.
 11. The system of claim 8, wherein the path leading to the left child node and the right child node each representing one of the two binary values.
 12. The system of claim 8, further configured to receive anonymized data from the destination computing device; and de-anonymize the received anonymized data by replacing the token value with data value.
 13. An anonymization system to anonymize data transmitted to a destination computing device, comprising: a logic to receive data to be transmitted to the destination from a user computer, the data including one or more characters; and an anonymization module to replace the data with a token representative of the data; and transmit the token to the destination computing device over a network; a data store with a tree of nodes, with each node representing a node value and a corresponding token value; and a logic to find a node with a node value equal to the value of the data and select the corresponding token value as the token representative of the data; wherein, if a node with a node value equal to the value of the data is not found, another node is added to the tree of nodes that represents the value of the data, with a corresponding token value; wherein, each node is configured to have a left child node and a right child node, with each of the path leading to the left child node and the right child node each representing a value; and the values of the paths followed to reach one of the nodes represents the corresponding token value for the one of the nodes; wherein the path leading to the left child node and the right child node each representing one of the two binary values; wherein, the data store stores a plurality of attributes associated with each node, a plurality of attributes including the node value, left boundary value, right boundary value, left child node value, right child node value and the token value; and wherein, if a node with a node value equal to the value of the data is not found, another node is added to the tree of nodes that represents the value of the data, with a corresponding token value, by locating a node with available child node having the following attributes: (x>left boundary value) and (x<right boundary value) and ((node data value>x and left child node value=null) or (node data value<x and right child node value=null)), where, x=value of the data.
 14. The system of claim 13, further configured to receive anonymized data from the destination computing device; and de-anonymize the received anonymized data by replacing the token with data value. 